Please keep in touch with the class by sending email to “comp
dash model at rams dot rutgers dot edu”. If you are not on this
mailing list, please ask Chung-chieh Shan to add you. You must be
on the list in order to post to it. All postings are archived.

Synopsis

Computational models are executable explanations of complex
phenomena. In this course, we will build and debug computational
models that include theories of perception that scientists argue
about as well as theories of the world that agents entertain to
figure out what to do. These explanations bring to life the
tradeoff between truth and beauty.

The first half of this course concerns interactive simulations
(games) and generalizations about their behavior
(types). The second half of this course concerns the
declarative vs procedural specification of hypotheses
(search) and their probabilities (weights).

Requirements

The requirements of this course are:

active participation in classes and the course mailing
list;

weekly or fortnightly homeworks that involve reading, writing,
and programming (the programming can ideally be done in
pairs);

three group projects, and their presentation and critique,
completed in varying groups of 3 to 5 students.

The central place of skills development in the class means that
typical students will not be well served by attending as if they
were auditors.

Audience

This course is for students both in and outside of computer
science who want to collaborate on interdisciplinary research. It
requires the ability to talk and think computationally about
programs, data, processes, abstraction, and proofs, such as taught
and exercised in the course “Computational Thinking” (16:198:503).
While the course will not count towards graduate requirements in
computer science (other than the graduate school’s general
requirement that PhD students take 48 credits of coursework), it
aims to prepare students for Rutgers’s advanced graduate courses in
artificial intelligence.

4/14

I’ll be traveling, but Matthew Stone will attend your
presentation of project 2 and also give a
related guest lecture.

To focus your project and presentation, please write an abstract
for it (as explained
by Landes) and send it to the class mailing list. This group
homework will help you the most if you do it before your
presentation, but it is due on Friday 4/17.

After your presentation, it is also time to start on the final
project, which concerns constraint satisfaction. Before our next
class meeting on 4/21, please read Chapter 4, up to and including
Section 4.1.1, of the textbook A guided tour of computer vision by Vishvjit S.
Nalwa.